Update README.md

README.md

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 ---
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: instruction
-    dtype: string
-  - name: input
-    dtype: string
-  - name: output
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 1237858832
-    num_examples: 413466
-  download_size: 539936581
-  dataset_size: 1237858832
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+license: gpl-2.0
+task_categories:
+  - code-generation
+  - text-generation
+language:
+  - en
+tags:
+  - linux
+  - kernel
+  - c
+  - c++
+  - bug-fix
+  - SFT
+  - instruction-tuning
+  - security
+size_categories:
+  - 100K<n<1M
 ---
+
+# 🐧 Linux Kernel Bugfixes & Patches Dataset (Instruction-Tuned)
+
+## 📖 Dataset Description
+
+This dataset is a highly curated, instruction-tuned collection of **problem-solution pairs** extracted directly from the official Linux Kernel Git repository (`torvalds/linux`). It is specifically designed to train Large Language Models (LLMs) on **low-level C programming, kernel architecture, memory management, and security vulnerability patching**.
+
+Unlike raw commit histories, this dataset has been strictly filtered to include only meaningful bug fixes and excludes noisy data such as typos, documentation updates, and version bumps.
+
+- **Language:** English (Commit messages), C / Assembly (Code)
+- **License:** GPL-2.0 (Inherited from Linux Kernel)
+- **Format:** Supervised Fine-Tuning (SFT) ready (Instruction / Input / Output)
+
+## 🚀 Intended Uses & Applications
+
+This dataset is perfect for training AI Agents and local LLMs for:
+1. **Low-Level Code Repair:** Teaching models to fix memory leaks, use-after-free errors, null pointer dereferences, and race conditions.
+2. **Patch Generation:** Generating valid `git diff` outputs based on issue descriptions.
+3. **Cybersecurity Analysis:** Fine-tuning models to recognize and patch CVEs and kernel panics.
+
+## 📊 Dataset Structure
+
+Each row in the dataset represents a single bug-fix commit. The data is formatted for immediate use in SFT pipelines (like Unsloth, QLoRA, Axolotl).
+
+**Example Instance:**
+```json
+{
+  "id": "a1b2c3d4e5f6...",
+  "instruction": "Analyze and fix the following issue in the Linux kernel code: net: sched: fix memory leak in tc_action",
+  "input": "Problem Details:\nWhen a filter action fails to initialize, the memory allocated for option parameters is not properly freed, resulting in a memory leak under high load.",
+  "output": "```diff\n--- a/net/sched/act_api.c\n+++ b/net/sched/act_api.c\n@@ -120,6 +120,7 @@ static int tcf_action_init(struct net *net...\n+    kfree(opt);\n```"
+}